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MACS 30000 - Perspectives on Computational Analysis

Dr. Benjamin Soltoff Ryan C. Hughes (TA) Joshua G. Mausolf (TA)
Office 249 Saieh Hall 251 Saieh Hall 251 Saieh Hall
Office Hours Th 1-3pm M 8:00-10:00am F 9:30-11:30am
GitHub bensoltoff rchughes jmausolf
  • Meeting day/time: MW 11:30-1:20pm, 247 Saieh Hall for Economics
  • Lab session: W 4:30-5:20pm, 247 Saieh Hall for Economics
  • Office hours also available by appointment

Course description

Massive digital traces of human behavior and ubiquitous computation have both extended and altered classical social science inquiry. This course surveys successful social science applications of computational approaches to the representation of complex data, information visualization, and model construction and estimation. We will reexamine the scientific method in the social sciences in context of both theory development and testing, exploring how computation and digital data enables new answers to classic investigations, the posing of novel questions, and new ethical challenges and opportunities. Students will review fundamental research designs such as observational studies and experiments, statistical summaries, visualization of data, and how computational opportunities can enhance them. The focus of the course is on exploring the wide range of contemporary approaches to computational social science, with practical programming assignments to train with these approaches.

Required textbooks

All textbooks are available in electronic editions either directly from the author or via the UChicago library (authentication required). Hardcopies can be purchased at your preferred retailer.


Assignment Quantity Points Total Points
Short assignments 8 10 80
Final exam 1 20 20
  • Short assignments will vary depending on subject matter. They could include writing assignments analyzing computational research designs and/or problem sets implementing specific computational methods.
  • Final exam will be a timed take-home exam. Details to be furnished near the end of term.

Disability services

If you need any special accommodations, please provide me (Dr. Soltoff) with a copy of your Accommodation Determination Letter (provided to you by the Student Disability Services office) as soon as possible so that you may discuss with me how your accommodations may be implemented in this course.

Course schedule (lite)

# Date Topic Assignment due
1. Mon, Sep. 25 Introduction to Computational Social Science
2. Wed, Sep. 27 Science in a computational era
3. Mon, Oct. 2 Observational data - counting things
4. Wed, Oct. 4 Observational data - measurement
5. Mon, Oct. 9 Observational data - forecasting
6. Wed, Oct. 11 Observational data - approximating experiments
7. Mon, Oct. 16 Asking questions - fundamentals Proposing an observational study
8. Wed, Oct. 18 Asking questions - digital enrichment
9. Mon, Oct. 23 Experiments Proposing a survey study
10. Wed, Oct. 25 Experiments
11. Mon, Oct. 30 Simulated data Proposing an experiment
12. Wed, Nov. 1 Simulated data
13. Mon, Nov. 6 Collaboration Simulating your income
14. Wed, Nov. 8 Collaboration
15. Mon, Nov. 13 Ethics Collaboration
16. Wed, Nov. 15 Ethics
17. Mon, Nov. 20 Exploratory data analysis - univariate visualizations The ethics of the Montana election experiment
18. Wed, Nov. 22 Exploratory data analysis - multivariate visualizations
19. Mon, Nov. 27 Exploratory data analysis - clustering Exploring the General Social Survey
20. Wed, Nov. 29 Exploratory data analysis - dimension reduction
21. Mon, Dec. 4 Unsupervised learning

The final exam will be distributed on Tuesday December 5 at 12pm and must be submitted by 11:59pm Wednesday December 6.

Course schedule (readings)

All readings are required unless otherwise noted. Adjustments can be made throughout the quarter; be sure to check this repository frequently to make sure you know all the assigned readings.

  1. Introduction to computational social science
  2. Social science in a computational era
  3. Observational data (counting things)
  4. Observational data (measurement)
  5. Observational data (forecasting)
  6. Observational data (approximating experiments)
  7. Asking questions (fundamentals)
  8. Asking questions (digitally-enriched)
  9. Experiments
  10. Experiments (more)
  11. Simulated data
    • "Indirect Inference," New Palgrave Dictionary of Economics
    • Benoit, Kenneth, "Simulation Methodologies for Political Scientists," The Political Methodologist, 10:1, pp. 12-16.
    • Recommended readings on simulation methods (not required for class)
      • Wolpin, Kenneth I., The Limits of Inference without Theory, MIT Press, 2013.
      • Davidson, Russell and James G. MacKinnon, "Section 9.6: The Method of Simulated Moments," Econometric Theory and Methods, Oxford University Press, 2004.
  12. Simulated data (cont.)
  13. Collaboration
  14. Collaboration (cont.)
  15. Ethics
  16. Ethics (cont.)
  17. Exploratory data analysis
  18. Exploratory data analysis (cont.)
  19. Exploratory data analysis - dimension reduction
  20. Exploratory data analysis - clustering


Course materials for MACS 30000 (Perspectives on Computational Analysis)






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